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Research on the Fusion Algorithm of Drone Images and Satellite Imagery

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Communications, Signal Processing, and Systems (CSPS 2023)

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 1033))

  • 121 Accesses

Abstract

With the rapid development of drone technology, drone imagery has become an important means of obtaining high-resolution surface information. However, due to the operational height and range limitations of drones, there are issues of limited coverage and small data volume in drone imagery. Meanwhile, satellite imagery offers extensive coverage and a large amount of data but with lower resolution. In order to fully utilize the advantages of drone imagery and satellite imagery, and improve the accuracy of surface information extraction and spatial resolution, researchers have conducted studies on the fusion algorithms of drone imagery and satellite imagery. This article provides a review and analysis of the fusion algorithms for drone imagery and satellite imagery. Firstly, the characteristics and advantages of drone imagery and satellite imagery are introduced, emphasizing the importance of integrating the two. Furthermore, data loading and preprocessing techniques are discussed. Then, common fusion methods for drone imagery and satellite imagery are detailed, including pixel-level fusion, feature-level fusion, and decision-level fusion, among others. The evaluation methods for fusion quality are also explained. Finally, research achievements from both domestic and international sources are presented.

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References

  1. Beigi P, Rajabi MS, Aghakhani S (2022) An overview of drone energy consumption factors and models. arXiv preprint arXiv:2206.10775

  2. Khalaf OI, Romero CAT, Hassan S et al (2022) Mitigating hotspot issues in heterogeneous wireless sensor networks. J Sens 2022:1–14

    Article  Google Scholar 

  3. Michałowska K, Głowienka E (2022) Multi-temporal analysis of changes of the southern part of the Baltic sea coast using aerial remote sensing data. Remote Sens 14(5):1212

    Article  Google Scholar 

  4. Chen T, Song C, Zhan P et al (2022) Remote sensing estimation of the flood storage capacity of basin-scale lakes and reservoirs at high spatial and temporal resolutions. Sci Total Environ 807:150772

    Article  Google Scholar 

  5. Zhu Q, Guo X, Deng W et al (2022) Land-use/land-cover change detection based on a Siamese global learning framework for high spatial resolution remote sensing imagery. ISPRS J Photogramm Remote Sens 184:63–78

    Article  Google Scholar 

  6. Yang D, Guo J, Sun S et al (2022) An interval decomposition-ensemble approach with data-characteristic-driven reconstruction for short-term load forecasting. Appl Energy 306:117992

    Article  Google Scholar 

  7. Xiao Z, Gang W, Yuan J et al (2022) Impacts of data preprocessing and selection on energy consumption prediction model of HVAC systems based on deep learning. Energy Build 258:111832

    Article  Google Scholar 

  8. Alseelawi N, Hazim HT, Salim ALRikabi HTH (2022) A novel method of multimodal medical image fusion based on hybrid approach of NSCT and DTCWT. Int J Online Biomed Eng 18(3)

    Google Scholar 

  9. Chen S, Chen J, Rao Y et al (2022) A hierarchical consensus attention network for feature matching of remote sensing images. IEEE Trans Geosci Remote Sens 60:1–11

    Google Scholar 

  10. Azam MA, Khan KB, Salahuddin S et al (2022) A review on multimodal medical image fusion: compendious analysis of medical modalities, multimodal databases, fusion techniques and quality metrics. Comput Biol Med 144:105253

    Article  Google Scholar 

  11. Jose J, Gautam N, Tiwari M et al (2021) An image quality enhancement scheme employing adolescent identity search algorithm in the NSST domain for multimodal medical image fusion. Biomed Signal Process Control 66:102480

    Article  Google Scholar 

  12. Javan FD, Samadzadegan F, Mehravar S et al (2021) A review of image fusion techniques for pan-sharpening of high-resolution satellite imagery. ISPRS J Photogramm Remote Sens 171:101–117

    Article  Google Scholar 

  13. Ding K, Ma K, Wang S et al (2021) Comparison of full-reference image quality models for optimization of image processing systems. Int J Comput Vis 129:1258–1281

    Article  Google Scholar 

  14. Liu J, Shang J, Liu R et al (2022) Attention-guided global-local adversarial learning for detail-preserving multi-exposure image fusion. IEEE Trans Circuits Syst Video Technol 32(8):5026–5040

    Article  Google Scholar 

  15. Wu S, An Y, Liu S et al (2015) Research and analysis of image fusion of UAV high resolution data and Landsat-8 multispectral data. J Guizhou Normal Univ (from Nat Sci Ed) 33(1):13–17

    Google Scholar 

  16. Chen L, Liao A (2008) Comparison and analysis of fusion of aerial photo digitized image and SPOT5 multispectral image. Geogr Inf World 6(3):21–25

    Google Scholar 

  17. Jia Y, Sun J (1997) Research on the fusion method of remote sensing multispectral image data and aerial photo digital image. Surveying Mapp Bull (5):10–12

    Google Scholar 

  18. Niu L, Li Y, Yang S et al (2019) Fusion algorithm for UAV aerial photo and satellite imagery. Remote Sens Inf 34(04):74–78

    Google Scholar 

  19. Masi G, Cozzolino D, Verdoliva L et al (2016) Pansharpening by convolutional neural networks. Remote Sens 8(7):594

    Article  Google Scholar 

  20. Zhong J, Yang B, Huang G et al (2016) Remote sensing image fusion with convolutional neural network. Sens Imaging 17(1):10

    Google Scholar 

  21. Li H, Liu F, Yang S et al (2016) Learning networks based on deep support values remote sensing image fusion of remote sensing. J Comput Sci 39(8):1583–1596

    Google Scholar 

  22. Jiang C, Zhang H, Shen H et al (2014) Two-step sparse coding for the pan-sharpening of remote sensing images. IEEE J Sel Top Appl Earth Observations Remote Sens 7(5):1792–1805

    Google Scholar 

  23. Chen Y, Sun K, Yin J et al (2017) GF-2 image fusion method quality evaluation. Surveying Mapp Sci (11):1–10

    Google Scholar 

  24. Tu R, He H, Shi B, Gan Y, Xu L (2018) Routine fusion evaluation of low-altitude UAV images and Ziyuan No. 3 images. Jiangxi Sci 36(02):324–330. https://doi.org/10.13990/j.issn1001-3679.2018.02.025

  25. Li Y, Yan W, An S et al (2023) A spatio-temporal fusion framework of UAV and satellite imagery for winter wheat growth monitoring. Drones 7(1):23

    Article  Google Scholar 

  26. Zhao F, Wu X, Wang S (2020) Object-oriented vegetation classification method based on UAV and satellite image fusion. Procedia Comput Sci 174:609–615

    Article  Google Scholar 

  27. Li Z, Li E, Samat A et al (2022) An object-oriented CNN model based on improved superpixel segmentation for high-resolution remote sensing image classification. IEEE J Sel Top Appl Earth Observations Remote Sens 15:4782–4796

    Article  Google Scholar 

  28. Zhang Y (2008) Methods for image fusion quality assessment-a review, comparison and analysis. Int Arch Photogramm Remote Sens Spat Inf Sci 37(PART B7):1101–1109

    Google Scholar 

  29. Shi W, Zhu CQ, Tian Y et al (2005) Wavelet-based image fusion and quality assessment. Int J Appl Earth Obs Geoinf 6(3–4):241–251

    Google Scholar 

  30. Bampis CG, Li Z, Bovik AC (2018) Spatiotemporal feature integration and model fusion for full reference video quality assessment. IEEE Trans Circuits Syst Video Technol 29(8):2256–2270

    Article  Google Scholar 

Download references

Acknowledgement

This work was supported by the National Natural Science Foundation of China (NSFC) (62001328).

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Authors and Affiliations

  1. Tianjin Key Laboratory of Wireless Mobile Communications and Power Transmission, Tianjin Normal University, Tianjin, 300387, China

    Xinwei Dong, Guowei Che, Chao Sun, Ruotong Zou, Lezhou Feng & Xiaoming Ding

Authors
  1. Xinwei Dong
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  2. Guowei Che
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  3. Chao Sun
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  4. Ruotong Zou
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  5. Lezhou Feng
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  6. Xiaoming Ding
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Corresponding author

Correspondence to Guowei Che .

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Editors and Affiliations

  1. College of Electronic and Communication Engineering, Tianjin Normal University, Tianjin, China

    Wei Wang

  2. Inovative Parking Building, Room B410, Dalian University of Technology, Dalian, China

    Xin Liu

  3. Sci & Tech, DianHang Bldg, Rm 321, Dalian Maritime Univ, Sch of Info, Dalian, China

    Zhenyu Na

  4. College of Electronic and Communication Engineering, Tianjin Normal University, Tianjin, China

    Baoju Zhang

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© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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Dong, X., Che, G., Sun, C., Zou, R., Feng, L., Ding, X. (2024). Research on the Fusion Algorithm of Drone Images and Satellite Imagery. In: Wang, W., Liu, X., Na, Z., Zhang, B. (eds) Communications, Signal Processing, and Systems. CSPS 2023. Lecture Notes in Electrical Engineering, vol 1033. Springer, Singapore. https://doi.org/10.1007/978-981-99-7502-0_56

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  • DOI: https://doi.org/10.1007/978-981-99-7502-0_56

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-99-7555-6

  • Online ISBN: 978-981-99-7502-0

  • eBook Packages: EngineeringEngineering (R0)

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